1. Field of the Invention
The present invention relates to a method of graphically representing statistical data derived from the quality control of testing carried out by analysis laboratories. The method allows a quick and easy comparison of an analysis laboratory to all analysis laboratories for determining the quality and the reliability of results from an analysis laboratory.
2. Description of Related Art
Quality control of analysis laboratories in the biomedical sector, food sector, etc. is of constant importance. For instance, in a clinical biochemical analysis, an erroneous collection of data can suggest a pathology which in reality is non-existent, or masks an existent pathology. It does not take a businessman to realize the loss in man-hours, money, etc. which can occur from such a collection of erroneous data.
The concerns of time, money and efficiency demand the highest quality from analysis laboratories. Therefore, monitoring and evaluating the reliability of results produced by analysis laboratories is central to maintaining quality control. The art of statistics provides a convenient basis for carrying out the needed monitoring and evaluation functions of the required quality control.
The statistical view sees each analysis laboratory as a "signal intermediary", an instrument constructed for transforming "signals" present in test materials into comprehensible and interpretable "messages". In the statistical view, a "signal" collected and intermediated by an analysis laboratory does not constitute information until received, interpreted, and integrated. Consequently, an analysis laboratory, as the "signal intermediary", adds its own "noise" component to the collected signal.
This "noise" component, introduced by an analysis laboratory into the "signal" present in the tested material, must be suitably evaluated and reduced as much as possible by human intervention aimed to improve regulation of analysis equipment. Therefore, each analysis laboratory needs continuous individual analysis which involves comparison with other analysis laboratories, for improving the validity of test results.
Statistical indices such as the mean and standard deviation from the mean (or quadratic deviation), are useful in fulfilling the need for monitoring and evaluating individual laboratory analysis. Furthermore, the larger the number of analysis laboratories, "samples", participating in a quality control program, the more precise the statistical data generated for the statistical indices; i.e., the closer the statistical data comes to the statistical data for the entire "population" of analysis laboratories. Typically, results of analysis carried out on the same object or objects are the test results used in generating the statistical data for each statistical index against which a designated analysis laboratory is compared. Preferably, statistical data is generated using test data, referred to as "historical data", on two different objects, substances, or the same substance at two different concentrations. This allows better evaluation of analysis laboratory performance.
Computers organize the historical data, generate the statistical data, and present the results thereof automatically. A graphic presentation of the results, which allows quick and easy summary and evaluation of the reliability of data produced by an analysis laboratory is preferred. Conventional systems for quality control of analysis laboratories employ essentially two types of graphic presentation:
(1) a totally numeric table, wherein the historical and statistical data are presented in their numeric form organized into a table as shown in FIG. 1; and PA1 (2) numeric and partly graphic table, wherein the historic data and statistical data are represented in a less detailed numeric table which is supplemented by a graph as shown in FIG. 2.
The numeric table of FIGS. 1A-1B presents the historical data on chemico-clinical analysis generated weekly for a three month period by a designated analysis laboratory. Test results are not readily ascertainable. An evaluator must digest numerous numeric values to gain insight into the quality of laboratory performance. Additionally, a direct comparison with other analysis laboratories is not possible from the numeric table of FIGS. 1A-1B. Several such tables need analyzing to make such a comparison.
The graphic representation of FIG. 2 provides an improved presentation over that of FIGS. 1A-1B. The graphic representation of FIG. 2 splits the representation into a less detailed numeric table on the right half of the page, and a graph or diagram, known as a Youden graph on the left half of the page. The graphic representation is printed on a sheet of paper in landscape.
A Youden graph represents the historical data for each laboratory. FIG. 2 illustrates a Youden graph of the historical data produced by each analysis laboratory in the testing of two substances or the same substance at two different concentrations. The Youden graph represents historical data based on the historical data's relation to the mean and standard deviation for the historical data of all the analysis laboratories. The Youden graph has a Cartesian coordinate system with the horizontal axis and the vertical axis corresponding to the two tested substances, respectively. Each axis is indexed by the mean and standard deviations of the historical data for the corresponding substance. The mean is centered on the axis and the standard deviations are indexed therefrom.
In FIG. 2 the Youden graph is divided into four quadrants by two perpendicular lines corresponding to the mean value on the X-axis, horizontal axis, and the mean value of the Y-axis, vertical axis. Furthermore, the corners of a box enclosing values within one and two standard deviation, as shown in FIG. 2, are printed on the Youden graph. In FIG. 2 the historical data from the various analysis laboratories are represented in the Youden graph with numbers and letters. The analysis laboratory under evaluation is marked with the letter X. Analysis laboratories whose historical data fall within the two standard deviation box are considered reliable. Analysis laboratories whose historical data fall outside the two standard deviation box are determined as laboratories which need to take steps to improve their test results.
The two squares designating the first and second standard deviation from the mean also include historical data which are actually outside the first and/or second standard deviation. These historical data are more than half a box length from the mean (more than a standard deviation from the mean); but are less than half a diagonal of the box from the mean. Therefore, these data point fall within the standard deviation box. Preferably, a circle representing the true first and second standard deviations should be printed on the Youden graph; however, the printers used by these conventional methods are incapable of complex printing, such as circles.